library(Seurat)
library(scHolography)
scHolography.obj <-readRDS("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/scHolography.obj_new.rds")
spatial.neighbor.Dermal <- findSpatialNeighborhood(scHolography.obj ,annotationToUse = "celltype",query.cluster = c("Dermal"),orig.assay = "RNA")
Warning: Invalid name supplied, making object name syntactically valid. New object name is Seurat..SCTransform.RNA; see ?make.names for more details on syntax validity
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix
|
| | 0%
|
|===================================================================== | 50%
|
|==========================================================================================================================================| 100%
Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~00s
|+++ | 5 % ~00s
|+++ | 6 % ~00s
|++++ | 7 % ~00s
|+++++ | 8 % ~00s
|+++++ | 9 % ~00s
|++++++ | 10% ~00s
|++++++ | 12% ~00s
|+++++++ | 13% ~00s
|+++++++ | 14% ~00s
|++++++++ | 15% ~00s
|+++++++++ | 16% ~00s
|+++++++++ | 17% ~00s
|++++++++++ | 19% ~00s
|++++++++++ | 20% ~00s
|+++++++++++ | 21% ~00s
|++++++++++++ | 22% ~00s
|++++++++++++ | 23% ~00s
|+++++++++++++ | 24% ~00s
|+++++++++++++ | 26% ~00s
|++++++++++++++ | 27% ~00s
|++++++++++++++ | 28% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++ | 30% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++ | 34% ~00s
|++++++++++++++++++ | 35% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 37% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 41% ~00s
|+++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|+++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s
Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 7 % ~01s
|++++ | 8 % ~01s
|+++++ | 9 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|+++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s
Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~00s
|++ | 3 % ~00s
|+++ | 4 % ~00s
|+++ | 6 % ~00s
|++++ | 7 % ~00s
|+++++ | 8 % ~00s
|+++++ | 10% ~00s
|++++++ | 11% ~00s
|+++++++ | 13% ~00s
|++++++++ | 14% ~00s
|++++++++ | 15% ~00s
|+++++++++ | 17% ~00s
|++++++++++ | 18% ~00s
|++++++++++ | 20% ~00s
|+++++++++++ | 21% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++ | 24% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++ | 28% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++ | 32% ~00s
|+++++++++++++++++ | 34% ~00s
|++++++++++++++++++ | 35% ~00s
|+++++++++++++++++++ | 37% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 39% ~00s
|+++++++++++++++++++++ | 41% ~00s
|++++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 11% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~00s
|+++++++++++++ | 24% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++ | 26% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++ | 28% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++ | 32% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++ | 34% ~00s
|++++++++++++++++++ | 35% ~00s
|++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 37% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 39% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 41% ~00s
|++++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s
Only one SCT model is stored - skipping recalculating corrected counts
Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~34s
|++ | 2 % ~33s
|++ | 3 % ~33s
|+++ | 4 % ~33s
|+++ | 5 % ~35s
|++++ | 6 % ~34s
|++++ | 7 % ~34s
|+++++ | 8 % ~33s
|+++++ | 9 % ~32s
|++++++ | 10% ~32s
|++++++ | 11% ~32s
|+++++++ | 12% ~31s
|+++++++ | 13% ~31s
|++++++++ | 14% ~30s
|++++++++ | 15% ~30s
|+++++++++ | 16% ~29s
|+++++++++ | 17% ~29s
|++++++++++ | 18% ~28s
|++++++++++ | 19% ~28s
|+++++++++++ | 20% ~28s
|+++++++++++ | 21% ~27s
|++++++++++++ | 22% ~27s
|++++++++++++ | 23% ~27s
|+++++++++++++ | 24% ~26s
|+++++++++++++ | 26% ~26s
|++++++++++++++ | 27% ~25s
|++++++++++++++ | 28% ~25s
|+++++++++++++++ | 29% ~25s
|+++++++++++++++ | 30% ~24s
|++++++++++++++++ | 31% ~24s
|++++++++++++++++ | 32% ~24s
|+++++++++++++++++ | 33% ~23s
|+++++++++++++++++ | 34% ~23s
|++++++++++++++++++ | 35% ~23s
|++++++++++++++++++ | 36% ~22s
|+++++++++++++++++++ | 37% ~22s
|+++++++++++++++++++ | 38% ~21s
|++++++++++++++++++++ | 39% ~21s
|++++++++++++++++++++ | 40% ~21s
|+++++++++++++++++++++ | 41% ~20s
|+++++++++++++++++++++ | 42% ~20s
|++++++++++++++++++++++ | 43% ~20s
|++++++++++++++++++++++ | 44% ~19s
|+++++++++++++++++++++++ | 45% ~19s
|+++++++++++++++++++++++ | 46% ~19s
|++++++++++++++++++++++++ | 47% ~18s
|++++++++++++++++++++++++ | 48% ~18s
|+++++++++++++++++++++++++ | 49% ~18s
|+++++++++++++++++++++++++ | 50% ~17s
|++++++++++++++++++++++++++ | 51% ~17s
|+++++++++++++++++++++++++++ | 52% ~16s
|+++++++++++++++++++++++++++ | 53% ~16s
|++++++++++++++++++++++++++++ | 54% ~16s
|++++++++++++++++++++++++++++ | 55% ~15s
|+++++++++++++++++++++++++++++ | 56% ~15s
|+++++++++++++++++++++++++++++ | 57% ~15s
|++++++++++++++++++++++++++++++ | 58% ~14s
|++++++++++++++++++++++++++++++ | 59% ~14s
|+++++++++++++++++++++++++++++++ | 60% ~14s
|+++++++++++++++++++++++++++++++ | 61% ~13s
|++++++++++++++++++++++++++++++++ | 62% ~13s
|++++++++++++++++++++++++++++++++ | 63% ~13s
|+++++++++++++++++++++++++++++++++ | 64% ~12s
|+++++++++++++++++++++++++++++++++ | 65% ~12s
|++++++++++++++++++++++++++++++++++ | 66% ~12s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|+++++++++++++++++++++++++++++++++++ | 68% ~11s
|+++++++++++++++++++++++++++++++++++ | 69% ~10s
|++++++++++++++++++++++++++++++++++++ | 70% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~10s
|+++++++++++++++++++++++++++++++++++++ | 72% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~09s
|++++++++++++++++++++++++++++++++++++++ | 74% ~09s
|++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~08s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=34s
Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~10s
|+ | 2 % ~10s
|++ | 3 % ~10s
|++ | 4 % ~10s
|+++ | 5 % ~10s
|+++ | 6 % ~10s
|++++ | 7 % ~10s
|++++ | 8 % ~10s
|+++++ | 9 % ~09s
|+++++ | 10% ~09s
|++++++ | 11% ~09s
|++++++ | 12% ~09s
|+++++++ | 13% ~09s
|+++++++ | 14% ~09s
|++++++++ | 15% ~09s
|++++++++ | 16% ~09s
|+++++++++ | 17% ~09s
|+++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|++++++++++ | 20% ~08s
|+++++++++++ | 21% ~08s
|+++++++++++ | 22% ~08s
|++++++++++++ | 23% ~08s
|++++++++++++ | 24% ~08s
|+++++++++++++ | 25% ~08s
|+++++++++++++ | 26% ~08s
|++++++++++++++ | 27% ~08s
|++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|+++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|++++++++++++++++ | 32% ~07s
|+++++++++++++++++ | 33% ~07s
|+++++++++++++++++ | 34% ~07s
|++++++++++++++++++ | 35% ~07s
|++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 37% ~07s
|+++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|+++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~06s
|++++++++++++++++++++++ | 44% ~06s
|+++++++++++++++++++++++ | 45% ~06s
|+++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~05s
|+++++++++++++++++++++++++ | 50% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|+++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s
|++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|+++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|+++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s
Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~14s
|+ | 2 % ~14s
|++ | 3 % ~14s
|++ | 4 % ~14s
|+++ | 5 % ~14s
|+++ | 6 % ~14s
|++++ | 7 % ~14s
|++++ | 8 % ~13s
|+++++ | 9 % ~13s
|+++++ | 10% ~13s
|++++++ | 11% ~13s
|++++++ | 12% ~13s
|+++++++ | 13% ~13s
|+++++++ | 14% ~13s
|++++++++ | 15% ~12s
|++++++++ | 16% ~12s
|+++++++++ | 17% ~12s
|+++++++++ | 18% ~12s
|++++++++++ | 19% ~12s
|++++++++++ | 20% ~12s
|+++++++++++ | 21% ~12s
|+++++++++++ | 22% ~11s
|++++++++++++ | 23% ~11s
|++++++++++++ | 24% ~11s
|+++++++++++++ | 25% ~11s
|+++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~11s
|++++++++++++++ | 28% ~11s
|+++++++++++++++ | 29% ~10s
|+++++++++++++++ | 30% ~10s
|++++++++++++++++ | 31% ~10s
|++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~10s
|+++++++++++++++++ | 34% ~10s
|++++++++++++++++++ | 35% ~10s
|++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 37% ~09s
|+++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|++++++++++++++++++++ | 40% ~09s
|+++++++++++++++++++++ | 41% ~09s
|+++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~08s
|++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|+++++++++++++++++++++++ | 46% ~08s
|++++++++++++++++++++++++ | 47% ~08s
|++++++++++++++++++++++++ | 48% ~08s
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spatial.neighbor.Dermal$neighbor.marker %>%
group_by(cluster) %>%
top_n(n = 10, wt = avg_log2FC) -> top10
DoHeatmap(spatial.neighbor.Dermal$bulk.count.obj,assay = "SCT", features = top10$gene) + NoLegend()+viridis::scale_fill_viridis()
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.

spatial.neighbor.Dermal$sc.marker %>%
group_by(cluster) %>%
top_n(n = 10, wt = avg_log2FC) -> top10b
DoHeatmap(spatial.neighbor.Dermal$query.only.obj$scHolography.sc,assay = "SCT", features = top10b$gene,group.by = "spatial.neighborhood") + NoLegend()+viridis::scale_fill_viridis()
Warning: The following features were omitted as they were not found in the scale.data slot for the SCT assay: MYH11, ADH1B, MESTScale for fill is already present.
Adding another scale for fill, which will replace the existing scale.

library(RColorBrewer)
fib.sp.col <- c(c(colorRampPalette(brewer.pal(12,"Paired"))(10)[1],(brewer.pal(4,"Greens"))),colorRampPalette(brewer.pal(12,"Paired"))(10)[c(2,4:10)])
scene = list(camera = list(eye = list(x = -1, z = 0, y = 2)))
library(ggplot2)
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.spatial.neighbor.heatmap.tiff", units="in", width=8, height=9, res=300)
DoHeatmap(spatial.neighbor.Dermal$bulk.count.obj,assay = "SCT", features = top10$gene,group.colors = brewer.pal(4,"Greens")) + NoLegend()+viridis::scale_fill_viridis()+theme(axis.text = element_text(size = 16,face = "bold"))
dev.off()
library(RColorBrewer)
scene = list(camera = list(eye = list(x = -1, z = 0, y = 2)))
fib1 <- scHolography::scHolographyPlot(spatial.neighbor.Dermal$scHolography.obj,color.by = "spatial.neighborhood",color = fib.sp.col,cells = which(spatial.neighbor.Dermal$scHolography.obj$scHolography.sc$celltype%in%c("Basal","Dermal","Endothelial","Smooth Muscle")))%>% plotly::layout(scene = scene)
plotly::orca(fib1, "3d.fib.svg",width = 7,height = 5)
fib1
fib1234 <- spatial.neighbor.Dermal$query.only.obj$scHolography.sc
fib1234$spatial.neighborhood <-paste0("Dermal_",fib1234$spatial.neighborhood)
fib1234$spatial.neighborhood <- factor(fib1234$spatial.neighborhood ,levels = sort(unique(fib1234$spatial.neighborhood )))
fib1234 <- SetIdent(fib1234,value ="spatial.neighborhood")
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.peri.vln.mark.plot.tiff", units="in", width=5, height=5, res=300)
VlnPlot(fib1234,c("RGS5","NOTCH3","MYH11","ACTA2"),assay = "SCT",ncol = 2,cols = (brewer.pal(4,"Greens")))
dev.off()
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.1234.col.vln.mark.plot.tiff", units="in", width=5, height=2.5, res=300)
VlnPlot(fib1234,c("COL1A2","DCN"),assay = "SCT",ncol = 2,cols = (brewer.pal(4,"Greens")))
dev.off()
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.1234.four.stack.vln.mark.plot.tiff", units="in", width=5, height=6, res=600)
VlnPlot(fib1234,c("APCDD1","TWIST2","ADH1B","GREM1","ABCA8","IGF1","RGS5","NOTCH3"),assay = "SCT",stack = T,flip = T)+NoLegend()+theme(axis.text = element_text(size = 16,face = "bold"),axis.title.x = element_blank(),axis.title.y = element_text(size = 16,face = "bold") ,axis.text.x = element_text(angle = 30,hjust=1))+scale_fill_manual(values = c("#EDF8E9","#EDF8E9", "#BAE4B3" ,"#BAE4B3" ,"#74C476","#74C476", "#238B45", "#238B45"))
dev.off()
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_3rdDraft//clusterDist.Fib1.2.3.4.to.4_rev.tiff", units="in", width=5, height=5, res=600)
scHolography::clusterDistanceBoxplot(spatial.neighbor.Dermal$scHolography.obj,annotationToUse = "spatial.neighborhood",query.cluster.list = (paste0("Dermal_",c(1:4))),reference.cluster = c("Dermal_4"))+ggplot2::scale_fill_manual(values = (brewer.pal(4,"Greens"))[1:4])+
geom_signif(comparisons = my_comparisons,test.args = list(size=5),map_signif_level = TRUE)+NoLegend()+theme(axis.text = element_text(size = 16,face = "bold"),axis.title.x = element_blank(),axis.title.y = element_text(size = 20,face = "bold") ,axis.text.x = element_text(angle = 45,hjust=1))+ylab("SMN Distance")
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
dev.off()
null device
1
dyn.fib <-findGeneSpatialDynamics(spatial.neighbor.Dermal$scHolography.obj,query.cluster = paste0("Dermal_",c(1:3)),ref.cluster = c("Smooth Muscle"),annotationToUse = "spatial.neighborhood",assayToUse = "SCT")
spatialDynamicsFeaturePlot(spatial.neighbor.Dermal$scHolography.obj,query.cluster = paste0("Dermal_",c(1:3)),ref.cluster = c("Smooth Muscle"),annotationToUse = "spatial.neighborhood",geneOI = c(head(dyn.fib)$gene,tail(dyn.fib)$gene),assayToUse = "SCT")
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.spatial.neighbor.1-4.dyn.heat.tiff", units="in", width=12, height=8, res=300)
spatialDynamicsFeaturePlot(spatial.neighbor.Dermal$scHolography.obj,query.cluster=paste0("Dermal_",1:4),ref.cluster = c("Dermal_4"),annotationToUse = "spatial.neighborhood",geneOI = c("APCDD1","TWIST2","WNT5A","POSTN","SFRP2","GREM1","ADH1B","IGF1","ABCA8","CXCL12","MFAP5","RGS5","NOTCH3","ACTA2","MYH11"),assayToUse = "SCT",self.color = c((colorRampPalette(brewer.pal(12,"Paired"))(10)[1]),(brewer.pal(4,"Greens")),(colorRampPalette(brewer.pal(12,"Paired"))(10)[3:10])))
dev.off()
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.spatial.neighbor.distal.col6a5.tiff", units="in", width=5, height=4, res=300)
expressionByDistPlot(spatial.neighbor.Dermal$scHolography.obj,query.cluster=paste0("Dermal_",1:3),ref.cluster = c("Smooth Muscle"),annotationToUse = "spatial.neighborhood",geneOI = "COL6A5",assayToUse = "SCT")
dev.off()
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.spatial.neighbor.proximal.mfap5.tiff", units="in", width=5, height=4, res=300)
expressionByDistPlot(spatial.neighbor.Dermal$scHolography.obj,query.cluster=paste0("Dermal_",1:3),ref.cluster = c("Smooth Muscle"),annotationToUse = "spatial.neighborhood",geneOI = "MFAP5",assayToUse = "SCT")
dev.off()
fib.sub <- spatial.neighbor.Dermal$scHolography.obj$scHolography.sc
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin//dermal.spatial.cluster.tiff", units="in", width=4, height=4, res=300)
DimPlot(fib.sub,group.by = "spatial.neighborhood",reduction = "ind.umap",cells = which(fib.sub$celltype%in%"Dermal"))+ggtitle("Spatial Neighborhoods")+scale_colour_manual(values = brewer.pal(4,"Greens"))+NoLegend()
dev.off()
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin//dermal.seurat.cluster.tiff", units="in", width=4, height=4, res=300)
DimPlot(fib.sub,group.by = "orig.cluster",reduction = "ind.umap",cells = which(fib.sub$celltype%in%"Dermal"))+ggtitle("Seurat Cluster")+scale_color_brewer(palette = "Set1")+NoLegend()
dev.off()
---
title: "R Notebook"
output: html_notebook
---

```{r}
library(Seurat)
library(scHolography)
scHolography.obj <-readRDS("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/scHolography.obj_new.rds")

```

```{r}

spatial.neighbor.Dermal <- findSpatialNeighborhood(scHolography.obj ,annotationToUse = "celltype",query.cluster = c("Dermal"),orig.assay = "RNA")
```

```{r}
library(dplyr)
library(Seurat)

spatial.neighbor.Dermal$scHolography.obj$scHolography.sc$spatial.neighborhood[which(spatial.neighbor.Dermal$scHolography.obj$scHolography.sc$spatial.neighborhood%in%c(as.character(1:4)))] <- paste0("Dermal_",spatial.neighbor.Dermal$scHolography.obj$scHolography.sc$spatial.neighborhood[which(spatial.neighbor.Dermal$scHolography.obj$scHolography.sc$spatial.neighborhood%in%c(as.character(1:4)))])

fib.sp.col <-  c(c(colorRampPalette(RColorBrewer::brewer.pal(12,"Paired"))(10)[1],(RColorBrewer::brewer.pal(4,"Greens"))),colorRampPalette(RColorBrewer::brewer.pal(12,"Paired"))(10)[c(2,4:10)])
neighbor.comp <- scHolography::scHolographyNeighborCompPlot(spatial.neighbor.Dermal$scHolography.obj,annotationToUse = "spatial.neighborhood",query.cluster = paste0("Dermal_",1:4),color = fib.sp.col)
library(ggplot2)
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.spatial.neighbor.comp.tiff", units="in", width=5, height=6, res=600)
neighbor.comp$neighbor.comp.plot+theme(axis.text = element_text(size = 16,face = "bold"),axis.title.x = element_blank(),axis.title.y = element_text(size = 20,face = "bold") ,axis.text.x = element_text(angle = 30,hjust=1))+NoLegend()
dev.off()

```

```{r}
spatial.neighbor.Dermal$neighbor.marker %>%
  group_by(cluster) %>%
  top_n(n = 10, wt = avg_log2FC) -> top10
DoHeatmap(spatial.neighbor.Dermal$bulk.count.obj,assay = "SCT", features = top10$gene) + NoLegend()+viridis::scale_fill_viridis()
spatial.neighbor.Dermal$sc.marker %>%
  group_by(cluster) %>%
  top_n(n = 10, wt = avg_log2FC) -> top10b
DoHeatmap(spatial.neighbor.Dermal$query.only.obj$scHolography.sc,assay = "SCT", features = top10b$gene,group.by = "spatial.neighborhood") + NoLegend()+viridis::scale_fill_viridis()
library(RColorBrewer)

fib.sp.col <-  c(c(colorRampPalette(brewer.pal(12,"Paired"))(10)[1],(brewer.pal(4,"Greens"))),colorRampPalette(brewer.pal(12,"Paired"))(10)[c(2,4:10)])


scene = list(camera = list(eye = list(x = -1, z = 0, y = 2)))

fig1 <- scHolography::scHolographyPlot(spatial.neighbor.Dermal$scHolography.obj,color.by = "spatial.neighborhood",color = fib.sp.col)%>% plotly::layout(scene = scene)
plotly::orca(fig1, "3d.fib.all.celltype.svg",width = 7,height = 5)

fig2 <- scHolography::scHolographyPlot(spatial.neighbor.Dermal$scHolography.obj,color.by = "spatial.neighborhood",color = fib.sp.col, cells = which(spatial.neighbor.Dermal$scHolography.obj$scHolography.sc$celltype%in%c("Basal","Dermal","Smooth Muscle")))%>% plotly::layout(scene = scene)
plotly::orca(fig2, "3d.fib.basal.derm.sm.svg",width = 7,height = 5)

fig3 <- scHolography::scHolographyPlot(spatial.neighbor.Dermal$scHolography.obj,color.by = "spatial.neighborhood",color = fib.sp.col,cells = which(spatial.neighbor.Dermal$scHolography.obj$scHolography.sc$celltype%in%c("Dermal")))%>% plotly::layout(scene = scene)
plotly::orca(fig3, "3d.fib.alone.svg",width = 7,height = 5)

fig4 <- scHolography::scHolographyPlot(spatial.neighbor.Dermal$scHolography.obj,color.by = "spatial.neighborhood",color = fib.sp.col,cells = which(spatial.neighbor.Dermal$scHolography.obj$scHolography.sc$celltype%in%c("Dermal","Endothelial")))%>% plotly::layout(scene = scene)
plotly::orca(fig4, "3d.fib.endo.svg",width = 7,height = 5)

fig5 <- scHolography::scHolographyPlot(spatial.neighbor.Dermal$scHolography.obj,color.by = "celltype",cells = which(spatial.neighbor.Dermal$scHolography.obj$scHolography.sc$celltype%in%c("Basal","Dermal","Smooth Muscle")))%>% plotly::layout(scene = scene)
plotly::orca(fig5, "3d.fib.basal.derm.sm.og.type.svg",width = 7,height = 5)


fig1
fig2
fig3
fig4
fig5
```


```{r}
library(ggplot2)
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.spatial.neighbor.heatmap.tiff", units="in", width=8, height=9, res=300)
DoHeatmap(spatial.neighbor.Dermal$bulk.count.obj,assay = "SCT", features = top10$gene,group.colors = brewer.pal(4,"Greens")) + NoLegend()+viridis::scale_fill_viridis()+theme(axis.text = element_text(size = 16,face = "bold"))
dev.off()
```

```{r}
library(RColorBrewer)
scene = list(camera = list(eye = list(x = -1, z = 0, y = 2)))

fib1 <- scHolography::scHolographyPlot(spatial.neighbor.Dermal$scHolography.obj,color.by = "spatial.neighborhood",color = fib.sp.col,cells = which(spatial.neighbor.Dermal$scHolography.obj$scHolography.sc$celltype%in%c("Basal","Dermal","Endothelial","Smooth Muscle")))%>% plotly::layout(scene = scene)
plotly::orca(fib1, "3d.fib.svg",width = 7,height = 5)

fib1
```



```{r}
fib1234 <- spatial.neighbor.Dermal$query.only.obj$scHolography.sc
fib1234$spatial.neighborhood <-paste0("Dermal_",fib1234$spatial.neighborhood)
fib1234$spatial.neighborhood <- factor(fib1234$spatial.neighborhood ,levels = sort(unique(fib1234$spatial.neighborhood )))
fib1234 <- SetIdent(fib1234,value ="spatial.neighborhood")

tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.peri.vln.mark.plot.tiff", units="in", width=5, height=5, res=300)
VlnPlot(fib1234,c("RGS5","NOTCH3","MYH11","ACTA2"),assay = "SCT",ncol = 2,cols = (brewer.pal(4,"Greens")))
dev.off()

tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.1234.col.vln.mark.plot.tiff", units="in", width=5, height=2.5, res=300)
VlnPlot(fib1234,c("COL1A2","DCN"),assay = "SCT",ncol = 2,cols = (brewer.pal(4,"Greens")))
dev.off()

```
```{r}
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.1234.four.stack.vln.mark.plot.tiff", units="in", width=5, height=6, res=600)
VlnPlot(fib1234,c("APCDD1","TWIST2","ADH1B","GREM1","ABCA8","IGF1","RGS5","NOTCH3"),assay = "SCT",stack = T,flip = T)+NoLegend()+theme(axis.text = element_text(size = 16,face = "bold"),axis.title.x = element_blank(),axis.title.y = element_text(size = 16,face = "bold") ,axis.text.x = element_text(angle = 30,hjust=1))+scale_fill_manual(values = c("#EDF8E9","#EDF8E9", "#BAE4B3" ,"#BAE4B3" ,"#74C476","#74C476", "#238B45", "#238B45"))
dev.off()
```

```{r}
library(ggpubr)
my_comparisons <- list( c("Dermal_1", "Dermal_2"), c("Dermal_2", "Dermal_3"), c("Dermal_3", "Dermal_4") )

tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_3rdDraft//clusterDist.Fib1.2.3.4.to.4_rev.tiff", units="in", width=5, height=5, res=600)
scHolography::clusterDistanceBoxplot(spatial.neighbor.Dermal$scHolography.obj,annotationToUse = "spatial.neighborhood",query.cluster.list = (paste0("Dermal_",c(1:4))),reference.cluster = c("Dermal_4"))+ggplot2::scale_fill_manual(values = (brewer.pal(4,"Greens"))[1:4])+ 
  geom_signif(comparisons = my_comparisons,test.args = list(size=5),map_signif_level = TRUE)+NoLegend()+theme(axis.text = element_text(size = 16,face = "bold"),axis.title.x = element_blank(),axis.title.y = element_text(size = 20,face = "bold") ,axis.text.x = element_text(angle = 45,hjust=1))+ylab("SMN Distance")
dev.off()


```

```{r}
dyn.fib <-findGeneSpatialDynamics(spatial.neighbor.Dermal$scHolography.obj,query.cluster = paste0("Dermal_",c(1:3)),ref.cluster = c("Smooth Muscle"),annotationToUse = "spatial.neighborhood",assayToUse = "SCT")
spatialDynamicsFeaturePlot(spatial.neighbor.Dermal$scHolography.obj,query.cluster = paste0("Dermal_",c(1:3)),ref.cluster = c("Smooth Muscle"),annotationToUse = "spatial.neighborhood",geneOI = c(head(dyn.fib)$gene,tail(dyn.fib)$gene),assayToUse = "SCT")
```

```{r}

tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.spatial.neighbor.1-4.dyn.heat.tiff", units="in", width=12, height=8, res=300)
spatialDynamicsFeaturePlot(spatial.neighbor.Dermal$scHolography.obj,query.cluster=paste0("Dermal_",1:4),ref.cluster = c("Dermal_4"),annotationToUse = "spatial.neighborhood",geneOI = c("APCDD1","TWIST2","WNT5A","POSTN","SFRP2","GREM1","ADH1B","IGF1","ABCA8","CXCL12","MFAP5","RGS5","NOTCH3","ACTA2","MYH11"),assayToUse = "SCT",self.color = c((colorRampPalette(brewer.pal(12,"Paired"))(10)[1]),(brewer.pal(4,"Greens")),(colorRampPalette(brewer.pal(12,"Paired"))(10)[3:10])))
dev.off()
```


```{r}
tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.spatial.neighbor.distal.col6a5.tiff", units="in", width=5, height=4, res=300)
expressionByDistPlot(spatial.neighbor.Dermal$scHolography.obj,query.cluster=paste0("Dermal_",1:3),ref.cluster = c("Smooth Muscle"),annotationToUse = "spatial.neighborhood",geneOI = "COL6A5",assayToUse = "SCT")
dev.off()

tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin/Fib.spatial.neighbor.proximal.mfap5.tiff", units="in", width=5, height=4, res=300)
expressionByDistPlot(spatial.neighbor.Dermal$scHolography.obj,query.cluster=paste0("Dermal_",1:3),ref.cluster = c("Smooth Muscle"),annotationToUse = "spatial.neighborhood",geneOI = "MFAP5",assayToUse = "SCT")
dev.off()
```

```{r}
fib.sub <- spatial.neighbor.Dermal$scHolography.obj$scHolography.sc

tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin//dermal.spatial.cluster.tiff", units="in", width=4, height=4, res=300)
DimPlot(fib.sub,group.by = "spatial.neighborhood",reduction = "ind.umap",cells = which(fib.sub$celltype%in%"Dermal"))+ggtitle("Spatial Neighborhoods")+scale_colour_manual(values = brewer.pal(4,"Greens"))+NoLegend()
dev.off()

tiff("~/OneDrive - Northwestern University/Northwestern/Yi Lab/Manuscript/Code_2ndDraft/Human Skin//dermal.seurat.cluster.tiff", units="in", width=4, height=4, res=300)
DimPlot(fib.sub,group.by = "orig.cluster",reduction = "ind.umap",cells = which(fib.sub$celltype%in%"Dermal"))+ggtitle("Seurat Cluster")+scale_color_brewer(palette = "Set1")+NoLegend()
dev.off()
```

